Journal Ethics Statement Source Link
Check your required declarations and supplied approvals for missing supporting records.
Academic submission checks
US$11View skill
Submission Anonymization Residue Queue
Turn your manuscript metadata and blind-review checklist into a review with clear source references.
A downloadable skill for your AI agent.
Your result
Possible identifying remnants.
Source references, missing information, and questions to review stay with the findings.
How to use it
Add the skill to your AI
Open your download and follow the start guide. Use an AI agent that can load skill files and run the included tools.
Provide your records
Manuscript metadata and blind-review checklist. Confirm the case and period you want to check.
Ask for the report
Copy the request below into your agent. Add the files and any task details it needs.
Review and save
Check the findings against the original records. Choose where to save the report and follow up on open questions.
Example request
Use the Submission Anonymization Residue Queue skill. Review the attached records: manuscript metadata and blind-review checklist. Prepare this result: possible identifying remnants. Include a source reference for each finding. List missing information and questions that need my review. Ask me to confirm the scope and where to save the report before you start.
What it checks
It keeps recorded facts, assumptions, and unanswered questions separate. Each item retains the evidence a reviewer needs to make a decision.
What you provide
Manuscript metadata and blind-review checklist.
Details it records
- Metadata field
- Visible identifier
- Supplied blind-review rule
Read the full task logic
For this task, keep one review row for each file + anonymization interpretation. Record metadata field, visible identifier, and supplied blind-review rule. Keep the source location beside each value.
Compare the submitted files with the supplied journal requirements and author-approved metadata. Link each reported number or declaration to its source.
Formatting and arithmetic checks do not establish research validity. Mark unverified author claims and preserve unresolved reviewer comments.
This method checks evidence references only. Interpretation, semantic matching, causal analysis, or synthetic test design is performed by the agent and must be reviewed. No model evaluation is implied by a passing fixture test.
Example result
This synthetic example shows the included check in use. Your agent uses its findings to complete your task report.
What you provide
Synthetic example records
| Sample record | claim: Synthetic observation about metadata field; classification: inferred; question: What evidence can confirm this inference? |
|---|
What comes back
A reviewer must decide
- What evidence can confirm this inference?
This result comes from the checking tool in your download.
Read the source records
These are synthetic records from the bundled example.
- source-r1 ยท records/r1
{"claim": "Synthetic observation about metadata field", "classification": "inferred", "id": "r1", "key": "file + anonymization interpretation / SYNTHETIC-001", "question": "What evidence can confirm this inference?"}
Checked values
- claim: Synthetic observation about metadata field; classification: inferred; question: What evidence can confirm this inference?
Run this example
After purchase, extract the ZIP. Run these commands from the skill folder. Use a new output folder.
python3 scripts/checks.py
python3 scripts/audit.py assets/example.json \
--files-root assets/sample-files --output-dir example-resultExpected exit code: 1. Code 0 means no helper finding. Code 1 means a finding or review item. Code 2 means incomplete or invalid input.
The check uses the records you supply. Review each finding against the original documents.
What is saved
Ask your agent to save the completed task report in a folder you choose. The report template includes the result, source references, missing information, and next actions.
- A readable check report
- The local checking tool can save its findings as report.md.
- A data copy
- The same check can be saved as report.json for use in other tools. A local audit database is optional.
Tonotes does not receive your work documents through this website. Documents you share with an AI service follow that service's storage settings.
Your download
US$11 includes one skill in a ZIP with 18 files. You receive the instructions and tools to run the task with your own records.
- A start guide to help you set up the skill.
- Task instructions that tell your AI what to check.
- Checking tools with worked examples.
- A report template with space for evidence and next actions.
Setup and file details
Use an agent that supports SKILL.md instructions and Python 3.10 or later with SQLite. Your AI-agent service is separate.
- Start guide: 1 file
- START-HERE.md explains setup and the expected example result.
- Agent instructions: 1 file
- SKILL.md defines this task and its completion checks.
- Task references: 4 files
- The task guide, input contract, tools guide, and research notes.
- Python tools: 4 files
- Check records, convert CSV input, and run the local checks.
- Examples and templates: 8 files
- The input schema, synthetic cases, expected report, and report template.
Collect the records
Start with manuscript metadata and blind-review checklist. Confirm the case, period, and source versions before the review.
Extract the task fields
Use manuscript version, journal rule version, figure/table/reference ID to keep records distinct. The agent records the original value, an exact quote, and the source location.
Make review questions clear
Keep each claim, its classification, and the question that needs a reviewer. Run the Python helper on the structured records. Check each finding against its source.
Complete the report
Prepare the task result: possible identifying remnants. Include missing evidence, unresolved questions, and the next action for each open item.
Eight synthetic cases check the tool locally. Live-agent accuracy on real documents has not been tested.